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We explore a new sensor suite to provide a precise and robust navigation information, primarily intended for pedestrian localisation. We use an IMU sensor augmented with an array of magnetometers, called MIMU (for Magneto-Inertial measurement Unit) hereafter, and a single central camera as the vision sensor. The MIMU sensor has been shown in previous work to significantly improve the inertial dead-reckoning...
This paper aims to leverage magnetic information from a Magneto-Inertial Measurement Unit — an IMU sensor augmented with an array of magnetometers, called MIMU hereafter — in a vision/inertial navigation system (VINS). This ego-motion estimation problem is formulated as an optimization over a sliding window fusing data from the MIMU with features tracked in a monocular camera image stream. The novelty...
A solution to visuo-inertial filtering and estimation based on homography, angular velocity, and specific acceleration measurements is proposed. This corresponds to the typical situation of a mono-camera/IMU sensor facing a (locally) planar environment. By lifting the estimation state space to a higher-dimensionnal space, we show that the problem can be formulated as a linear estimation problem. This...
The paper concerns visuo-inertial filtering and estimation based on homography and angular velocity measurements, i.e. data obtained from a mono-camera/IMU sensor. We extend recently developed nonlinear filters on the special linear group of homographies to the estimation of scene parameters and velocity of the sensor. A validation of the proposed solution and a comparative evaluation based on real...
The Simultaneous Localisation And Mapping (SLAM) for a camera moving in a scene is a long term research problem. Here we improve a recent visual SLAM which applies Local Bundle Adjustments (LBA) on selected key-frames of a video: we show how to correct the scale drift observed in long monocular video sequence using an additional odometry sensor. Our method and results are interesting for several reasons:...
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